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   "source": [
    "# COVID-19 Deaths, Cases & Recovery Per Capita\n",
    "> Compare deaths and total cases adjusting for population size. \n",
    "\n",
    "- comments: true\n",
    "- author: Joao B. Duarte. Hamel Husain & Arun Gupta\n",
    "- categories: [growth, compare, interactive]\n",
    "- hide: false\n",
    "- image: images/covid-permillion-trajectories.png\n",
    "- permalink: /covid-compare-permillion/"
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    "#hide\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import altair as alt\n",
    "from load_covid_data import load_individual_timeseries\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "chart_width = 550\n",
    "chart_height= 400"
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    "tags": []
   },
   "source": [
    "## Deaths Per Million Of Inhabitants"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Since reaching at least 1 death per million\n",
    "\n",
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization."
   ]
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   "source": [
    "#hide \n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\", \n",
    "                   error_bad_lines=False)\n",
    "data = data.drop(columns=[\"Lat\", \"Long\"])\n",
    "data = data.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data = pd.DataFrame(data.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data.reset_index(inplace=True)  \n",
    "data = data.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_cases\"})\n",
    "data['date'] =pd.to_datetime(data.date)\n",
    "data = data.sort_values(by = \"date\")\n",
    "data.loc[data.location == \"US\",\"location\"] = \"United States\"\n",
    "data.loc[data.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "\n",
    "data_pwt = pd.read_stata(\"https://www.rug.nl/ggdc/docs/pwt91.dta\")\n",
    "\n",
    "filter1 = data_pwt[\"year\"] == 2017\n",
    "data_pop = data_pwt[filter1]\n",
    "data_pop = data_pop[[\"country\",\"pop\"]]\n",
    "data_pop.loc[data_pop.country == \"Republic of Korea\",\"country\"] = \"South Korea\"\n",
    "data_pop.loc[data_pop.country == \"Iran (Islamic Republic of)\",\"country\"] = \"Iran\"\n",
    "\n",
    "# per habitant\n",
    "data_pc = data.copy()\n",
    "countries = [\"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\", \"South Korea\", \"Japan\", \n",
    "             \"Brazil\", \"Iran\", 'Netherlands', 'Belgium', 'Sweden', \"Romania\",\n",
    "             'Switzerland', 'Norway', 'Denmark', 'Austria', 'Slovenia', 'Greece']\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "# compute per habitant\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_cases\"] = data_pc.loc[data_pc.location == i,\"total_cases\"]/float(data_pop.loc[data_pop.country == i, \"pop\"])\n",
    "\n",
    "    # get each country time series\n",
    "filter1 = data_pc[\"total_cases\"] > 1\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])"
   ]
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\"location\": \"Greece\", \"date\": \"2020-04-13T00:00:00\", \"total_cases\": 8.87114828655591, \"n_days\": 23, \"log_cases\": 2.182804245276986, \"trend_2days\": 7.971192576439371, \"trend_4days\": 3.9855962882196856, \"trend_12days\": 1.3285320960732283, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6574, \"location\": \"Greece\", \"date\": \"2020-04-14T00:00:00\", \"total_cases\": 9.050363403456029, \"n_days\": 24, \"log_cases\": 2.2028049119836552, \"trend_2days\": 8.317766166719343, \"trend_4days\": 4.1588830833596715, \"trend_12days\": 1.3862943611198906, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6575, \"location\": \"Greece\", \"date\": \"2020-04-15T00:00:00\", \"total_cases\": 9.139970961906089, \"n_days\": 25, \"log_cases\": 2.212657208426667, \"trend_2days\": 8.664339756999317, \"trend_4days\": 4.332169878499658, \"trend_12days\": 1.4440566261665526, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6576, \"location\": \"Greece\", \"date\": \"2020-04-16T00:00:00\", \"total_cases\": 9.408793637256268, \"n_days\": 26, \"log_cases\": 2.2416447452999195, \"trend_2days\": 9.010913347279288, \"trend_4days\": 4.505456673639644, \"trend_12days\": 1.5018188912132147, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6577, \"location\": \"Greece\", \"date\": \"2020-04-17T00:00:00\", \"total_cases\": 9.677616312606446, \"n_days\": 27, \"log_cases\": 2.2698156222666155, \"trend_2days\": 9.357486937559262, \"trend_4days\": 4.678743468779631, \"trend_12days\": 1.559581156259877, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6578, \"location\": \"Greece\", \"date\": \"2020-04-18T00:00:00\", \"total_cases\": 9.856831429506567, \"n_days\": 28, \"log_cases\": 2.288164760934812, \"trend_2days\": 9.704060527839234, \"trend_4days\": 4.852030263919617, \"trend_12days\": 1.617343421306539, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6579, \"location\": \"Greece\", \"date\": \"2020-04-19T00:00:00\", \"total_cases\": 10.125654104856745, \"n_days\": 29, \"log_cases\": 2.3150722138547364, \"trend_2days\": 10.050634118119207, \"trend_4days\": 5.025317059059604, \"trend_12days\": 1.675105686353201, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6581, \"location\": \"Greece\", \"date\": \"2020-04-20T00:00:00\", \"total_cases\": 10.394476780206924, \"n_days\": 30, \"log_cases\": 2.3412745862487605, \"trend_2days\": 10.39720770839918, \"trend_4days\": 5.19860385419959, \"trend_12days\": 1.732867951399863, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6582, \"location\": \"Greece\", \"date\": \"2020-04-21T00:00:00\", \"total_cases\": 10.842514572457222, \"n_days\": 31, \"log_cases\": 2.383474940739137, \"trend_2days\": 10.743781298679153, \"trend_4days\": 5.371890649339576, \"trend_12days\": 1.7906302164465253, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6583, \"location\": \"Greece\", \"date\": \"2020-04-22T00:00:00\", \"total_cases\": 10.842514572457222, \"n_days\": 32, \"log_cases\": 2.383474940739137, \"trend_2days\": 11.090354888959125, \"trend_4days\": 5.545177444479562, \"trend_12days\": 1.8483924814931874, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6584, \"location\": \"Greece\", \"date\": \"2020-04-23T00:00:00\", \"total_cases\": 11.20094480625746, \"n_days\": 33, \"log_cases\": 2.415998132444697, \"trend_2days\": 11.436928479239096, \"trend_4days\": 5.718464239619548, \"trend_12days\": 1.9061547465398494, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6585, \"location\": \"Greece\", \"date\": \"2020-04-24T00:00:00\", \"total_cases\": 11.648982598507759, \"n_days\": 34, \"log_cases\": 2.455218845597978, \"trend_2days\": 11.78350206951907, \"trend_4days\": 5.891751034759535, \"trend_12days\": 1.9639170115865117, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6586, \"location\": \"Greece\", \"date\": \"2020-04-25T00:00:00\", \"total_cases\": 11.648982598507759, \"n_days\": 35, \"log_cases\": 2.455218845597978, \"trend_2days\": 12.130075659799042, \"trend_4days\": 6.065037829899521, \"trend_12days\": 2.0216792766331735, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6587, \"location\": \"Greece\", \"date\": \"2020-04-26T00:00:00\", \"total_cases\": 12.007412832307999, \"n_days\": 36, \"log_cases\": 2.4855241950933076, \"trend_2days\": 12.476649250079015, \"trend_4days\": 6.238324625039508, \"trend_12days\": 2.0794415416798357, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6588, \"location\": \"Greece\", \"date\": \"2020-04-27T00:00:00\", \"total_cases\": 12.186627949208118, \"n_days\": 37, \"log_cases\": 2.500339280878448, \"trend_2days\": 12.823222840358987, \"trend_4days\": 6.411611420179494, \"trend_12days\": 2.137203806726498, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}], \"data-cf5cfaa2f9f67721e9c0baa1e72af7ac\": [{\"label\": \"Doubles every 2 days\", \"x_coord\": 6, \"y_coord\": 4.0}, {\"label\": \"Doubles every 4 days\", \"x_coord\": 16, \"y_coord\": 3.5}, {\"label\": \"Doubles every 12 days\", \"x_coord\": 25, \"y_coord\": 1.8}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Stack data to get it to Altair dataframe format\n",
    "data_countries_pc2 = data_countries_pc.copy()\n",
    "for i in range(0,len(countries)):\n",
    "    data_countries_pc2[i] = data_countries_pc2[i].reset_index()\n",
    "    data_countries_pc2[i]['n_days'] = data_countries_pc2[i].index\n",
    "    data_countries_pc2[i]['log_cases'] = np.log(data_countries_pc2[i][\"total_cases\"])\n",
    "data_plot = data_countries_pc2[0]\n",
    "for i in range(1, len(countries)):    \n",
    "    data_plot = pd.concat([data_plot, data_countries_pc2[i]], axis=0)\n",
    "data_plot[\"trend_2days\"] = np.log(2)/2*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_4days\"] = np.log(2)/4*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_12days\"] = np.log(2)/12*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_2days_label\"] = \"Doubles every 2 days\"\n",
    "data_plot[\"trend_4days_label\"] = \"Doubles evey 4 days\"\n",
    "data_plot[\"trend_12days_label\"] = \"Doubles every 12 days\"\n",
    "\n",
    "\n",
    "# Plot it using Altair\n",
    "source = data_plot\n",
    "\n",
    "scales = alt.selection_interval(bind='scales', zoom=False)\n",
    "selection = alt.selection_multi(fields=['location'], bind='legend')\n",
    "\n",
    "base = alt.Chart(source, title = \"COVID-19 Deaths Per Million of Inhabitants\").encode(\n",
    "    x = alt.X('n_days:Q', title = \"Days passed since reaching 1 death per million\"),\n",
    "    y = alt.Y(\"log_cases:Q\",title = \"Log of deaths per million\"),\n",
    "    color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                     scale=alt.Scale(scheme='tableau20')),\n",
    "    opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    ")\n",
    "\n",
    "lines = base.mark_line().add_selection(\n",
    "    scales\n",
    ").add_selection(\n",
    "    selection\n",
    ").properties(\n",
    "    width=chart_width,\n",
    "    height=chart_height\n",
    ")\n",
    "\n",
    "trend_2d = alt.Chart(source).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_2days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ").mark_line(color=\"grey\", strokeDash=[3,3])\n",
    "\n",
    "\n",
    "labels = pd.DataFrame([{'label': 'Doubles every 2 days', 'x_coord': 11.4, 'y_coord': 6.5},\n",
    "                       {'label': 'Doubles every 4 days', 'x_coord': 30.5, 'y_coord': 6.5},\n",
    "                       {'label': 'Doubles every 12 days', 'x_coord': 46, 'y_coord': 3},\n",
    "                      ])\n",
    "trend_label = (alt.Chart(labels)\n",
    "                    .mark_text(align='left', dx=-55, dy=-15, fontSize=12, color=\"grey\")\n",
    "                    .encode(x='x_coord:Q',\n",
    "                            y='y_coord:Q',\n",
    "                            text='label:N')\n",
    "                   )\n",
    "\n",
    "trend_4d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_4days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "trend_12d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_12days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "\n",
    "plot1= (\n",
    "(trend_2d + trend_4d + trend_12d + trend_label + lines)\n",
    ".configure_title(fontSize=20)\n",
    ".configure_axis(labelFontSize=15,titleFontSize=18)\n",
    ")\n",
    "#plot1.save((\"../images/covid-permillion-trajectories.png\"))\n",
    "plot1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014971,
     "end_time": "2020-04-28T12:13:13.062500",
     "exception": false,
     "start_time": "2020-04-28T12:13:13.047529",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Last Available Total Deaths By Country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-28T12:13:13.101917Z",
     "iopub.status.busy": "2020-04-28T12:13:13.100352Z",
     "iopub.status.idle": "2020-04-28T12:13:13.133631Z",
     "shell.execute_reply": "2020-04-28T12:13:13.134161Z"
    },
    "papermill": {
     "duration": 0.056931,
     "end_time": "2020-04-28T12:13:13.134308",
     "exception": false,
     "start_time": "2020-04-28T12:13:13.077377",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Deaths per Million</th>\n",
       "      <th>Log of Deaths per Million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>62.847344</td>\n",
       "      <td>4.140709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>630.570337</td>\n",
       "      <td>6.446625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>21.993587</td>\n",
       "      <td>3.090751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>74.473916</td>\n",
       "      <td>4.310449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>346.991922</td>\n",
       "      <td>5.849301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>74.603393</td>\n",
       "      <td>4.312186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>12.186628</td>\n",
       "      <td>2.500339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>71.535246</td>\n",
       "      <td>4.270190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>454.465040</td>\n",
       "      <td>6.119121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>3.019976</td>\n",
       "      <td>1.105249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>266.143251</td>\n",
       "      <td>5.584035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>38.639999</td>\n",
       "      <td>3.654288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>89.839728</td>\n",
       "      <td>4.498027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>2.452335</td>\n",
       "      <td>0.897041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>39.904305</td>\n",
       "      <td>3.686484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>4.785983</td>\n",
       "      <td>1.565691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>507.417642</td>\n",
       "      <td>6.229334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>229.448961</td>\n",
       "      <td>5.435681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>196.436892</td>\n",
       "      <td>5.280341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>319.681061</td>\n",
       "      <td>5.767324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>173.392996</td>\n",
       "      <td>5.155561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  Deaths per Million  Log of Deaths per Million\n",
       "Country                                                                 \n",
       "Austria        2020-04-27           62.847344                   4.140709\n",
       "Belgium        2020-04-27          630.570337                   6.446625\n",
       "Brazil         2020-04-27           21.993587                   3.090751\n",
       "Denmark        2020-04-27           74.473916                   4.310449\n",
       "France         2020-04-27          346.991922                   5.849301\n",
       "Germany        2020-04-27           74.603393                   4.312186\n",
       "Greece         2020-04-27           12.186628                   2.500339\n",
       "Iran           2020-04-27           71.535246                   4.270190\n",
       "Italy          2020-04-27          454.465040                   6.119121\n",
       "Japan          2020-04-27            3.019976                   1.105249\n",
       "Netherlands    2020-04-27          266.143251                   5.584035\n",
       "Norway         2020-04-27           38.639999                   3.654288\n",
       "Portugal       2020-04-27           89.839728                   4.498027\n",
       "Singapore      2020-04-27            2.452335                   0.897041\n",
       "Slovenia       2020-04-27           39.904305                   3.686484\n",
       "South Korea    2020-04-27            4.785983                   1.565691\n",
       "Spain          2020-04-27          507.417642                   6.229334\n",
       "Sweden         2020-04-27          229.448961                   5.435681\n",
       "Switzerland    2020-04-27          196.436892                   5.280341\n",
       "United Kingdom 2020-04-27          319.681061                   5.767324\n",
       "United States  2020-04-27          173.392996                   5.155561"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "label = 'Deaths'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name = f'{label} per Million'\n",
    "temp.columns = ['Country', 'date', metric_name]\n",
    "# temp.loc[:, 'month'] = temp.date.dt.strftime('%Y-%m')\n",
    "temp.loc[:, f'Log of {label} per Million'] = temp[f'{label} per Million'].apply(lambda x: np.log(x))\n",
    "\n",
    "temp.groupby('Country').last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2020-04-28T12:13:18.109363Z"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "# Get data and clean it\n",
    "\n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\", error_bad_lines=False)\n",
    "data = data.drop(columns=[\"Lat\", \"Long\"])\n",
    "data = data.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data = pd.DataFrame(data.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data.reset_index(inplace=True)  \n",
    "data = data.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_cases\"})\n",
    "data['date'] =pd.to_datetime(data.date)\n",
    "data = data.sort_values(by = \"date\")\n",
    "data.loc[data.location == \"US\",\"location\"] = \"United States\"\n",
    "data.loc[data.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "\n",
    "# Population data (last year is 2017 which is what we use)\n",
    "data_pwt = pd.read_stata(\"https://www.rug.nl/ggdc/docs/pwt91.dta\")\n",
    "\n",
    "filter1 = data_pwt[\"year\"] == 2017\n",
    "data_pop = data_pwt[filter1]\n",
    "data_pop = data_pop[[\"country\",\"pop\"]]\n",
    "data_pop.loc[data_pop.country == \"Republic of Korea\",\"country\"] = \"South Korea\"\n",
    "data_pop.loc[data_pop.country == \"Iran (Islamic Republic of)\",\"country\"] = \"Iran\"\n",
    "\n",
    "# per habitant\n",
    "data_pc = data.copy()\n",
    "\n",
    "# I can add more countries if needed\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\", \"Iran\", \"Romania\"]\n",
    "\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "# compute per habitant\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_cases\"] = data_pc.loc[data_pc.location == i,\"total_cases\"]/float(data_pop.loc[data_pop.country == i, \"pop\"])\n",
    "    \n",
    "# get each country time series\n",
    "filter1 = data_pc[\"total_cases\"] > 1\n",
    "\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])"
   ]
  },
  {
   "cell_type": "markdown",
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     "duration": 0.017036,
     "end_time": "2020-04-28T12:13:18.143953",
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     "start_time": "2020-04-28T12:13:18.126917",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Cases Per Million of Habitants\n",
    "\n",
    "Since reaching at least 1 case per million\n",
    "\n",
    "> Note: The following chart, \"Cases Per Million of Habitants\" is biased depending on how widely a country administers tests. Please read with caution.\n",
    "\n",
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization."
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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     "status": "completed"
    },
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   },
   "outputs": [
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23, \"log_cases\": 5.42402943494876, \"trend_2days\": 7.971192576439371, \"trend_4days\": 3.9855962882196856, \"trend_12days\": 1.3285320960732283, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7811, \"location\": \"Iran\", \"date\": \"2020-03-20T00:00:00\", \"total_cases\": 242.03209856884206, \"n_days\": 24, \"log_cases\": 5.489070356075174, \"trend_2days\": 8.317766166719343, \"trend_4days\": 4.1588830833596715, \"trend_12days\": 1.3862943611198906, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7812, \"location\": \"Iran\", \"date\": \"2020-03-21T00:00:00\", \"total_cases\": 253.9341046377436, \"n_days\": 25, \"log_cases\": 5.537074802800543, \"trend_2days\": 8.664339756999317, \"trend_4days\": 4.332169878499658, \"trend_12days\": 1.4440566261665526, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7813, \"location\": \"Iran\", \"date\": \"2020-03-22T00:00:00\", \"total_cases\": 266.6000075764918, \"n_days\": 26, \"log_cases\": 5.585749436163557, \"trend_2days\": 9.010913347279288, \"trend_4days\": 4.505456673639644, \"trend_12days\": 1.5018188912132147, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7814, \"location\": \"Iran\", \"date\": \"2020-03-23T00:00:00\", \"total_cases\": 283.9848218241316, \"n_days\": 27, \"log_cases\": 5.648920792451783, \"trend_2days\": 9.357486937559262, \"trend_4days\": 4.678743468779631, \"trend_12days\": 1.559581156259877, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7815, \"location\": \"Iran\", \"date\": \"2020-03-24T00:00:00\", \"total_cases\": 305.69427802848406, \"n_days\": 28, \"log_cases\": 5.7225855111177, \"trend_2days\": 9.704060527839234, \"trend_4days\": 4.852030263919617, \"trend_12days\": 1.617343421306539, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7816, \"location\": \"Iran\", \"date\": \"2020-03-25T00:00:00\", \"total_cases\": 332.87422149431916, \"n_days\": 29, \"log_cases\": 5.8077647053985615, \"trend_2days\": 10.050634118119207, \"trend_4days\": 5.025317059059604, \"trend_12days\": 1.675105686353201, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7817, \"location\": \"Iran\", \"date\": \"2020-03-26T00:00:00\", \"total_cases\": 362.3088928179276, \"n_days\": 30, \"log_cases\": 5.892497143053641, \"trend_2days\": 10.39720770839918, \"trend_4days\": 5.19860385419959, \"trend_12days\": 1.732867951399863, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7818, \"location\": \"Iran\", \"date\": \"2020-03-27T00:00:00\", \"total_cases\": 398.3598967077887, \"n_days\": 31, \"log_cases\": 5.987355859769773, \"trend_2days\": 10.743781298679153, \"trend_4days\": 5.371890649339576, \"trend_12days\": 1.7906302164465253, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7819, \"location\": \"Iran\", \"date\": \"2020-03-28T00:00:00\", \"total_cases\": 436.2590381859886, \"n_days\": 32, \"log_cases\": 6.0782361912068685, \"trend_2days\": 11.090354888959125, \"trend_4days\": 5.545177444479562, \"trend_12days\": 1.8483924814931874, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7820, \"location\": \"Iran\", \"date\": \"2020-03-29T00:00:00\", \"total_cases\": 472.0020191444599, \"n_days\": 33, \"log_cases\": 6.156983263424838, \"trend_2days\": 11.436928479239096, \"trend_4days\": 5.718464239619548, \"trend_12days\": 1.9061547465398494, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7822, \"location\": \"Iran\", \"date\": \"2020-03-30T00:00:00\", \"total_cases\": 511.25646152077485, \"n_days\": 34, \"log_cases\": 6.236871345948586, \"trend_2days\": 11.78350206951907, \"trend_4days\": 5.891751034759535, \"trend_12days\": 1.9639170115865117, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7823, \"location\": \"Iran\", \"date\": \"2020-03-31T00:00:00\", \"total_cases\": 549.5745141856648, \"n_days\": 35, \"log_cases\": 6.309144368263922, \"trend_2days\": 12.130075659799042, \"trend_4days\": 6.065037829899521, \"trend_12days\": 2.0216792766331735, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7830, \"location\": \"Iran\", \"date\": \"2020-04-01T00:00:00\", \"total_cases\": 586.3894149453727, \"n_days\": 36, \"log_cases\": 6.373984099497811, \"trend_2days\": 12.476649250079015, \"trend_4days\": 6.238324625039508, \"trend_12days\": 2.0794415416798357, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7841, \"location\": \"Iran\", \"date\": \"2020-04-02T00:00:00\", \"total_cases\": 621.8120520551986, \"n_days\": 37, \"log_cases\": 6.43263787996367, \"trend_2days\": 12.823222840358987, \"trend_4days\": 6.411611420179494, \"trend_12days\": 2.137203806726498, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7850, \"location\": \"Iran\", \"date\": \"2020-04-03T00:00:00\", \"total_cases\": 655.2633424041298, \"n_days\": 38, \"log_cases\": 6.485037204306973, \"trend_2days\": 13.16979643063896, \"trend_4days\": 6.58489821531948, \"trend_12days\": 2.19496607177316, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7851, \"location\": \"Iran\", \"date\": \"2020-04-04T00:00:00\", \"total_cases\": 686.8048905784444, \"n_days\": 39, \"log_cases\": 6.532050249816902, \"trend_2days\": 13.516370020918933, \"trend_4days\": 6.758185010459466, \"trend_12days\": 2.252728336819822, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7852, \"location\": \"Iran\", \"date\": \"2020-04-05T00:00:00\", \"total_cases\": 717.3977281240785, \"n_days\": 40, \"log_cases\": 6.575630398274209, \"trend_2days\": 13.862943611198906, \"trend_4days\": 6.931471805599453, \"trend_12days\": 2.3104906018664844, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7853, \"location\": \"Iran\", \"date\": \"2020-04-06T00:00:00\", \"total_cases\": 745.4154939632939, \"n_days\": 41, \"log_cases\": 6.613941772934971, \"trend_2days\": 14.209517201478878, \"trend_4days\": 7.104758600739439, \"trend_12days\": 2.368252866913146, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7854, \"location\": \"Iran\", \"date\": \"2020-04-07T00:00:00\", \"total_cases\": 771.1538901102248, \"n_days\": 42, \"log_cases\": 6.647887951714022, \"trend_2days\": 14.556090791758852, \"trend_4days\": 7.278045395879426, \"trend_12days\": 2.4260151319598084, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7855, \"location\": \"Iran\", \"date\": \"2020-04-08T00:00:00\", \"total_cases\": 795.7587618696413, \"n_days\": 43, \"log_cases\": 6.679296076933681, \"trend_2days\": 14.902664382038823, \"trend_4days\": 7.451332191019412, \"trend_12days\": 2.4837773970064707, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7856, \"location\": \"Iran\", \"date\": \"2020-04-09T00:00:00\", \"total_cases\": 815.891140665278, \"n_days\": 44, \"log_cases\": 6.704280940017275, \"trend_2days\": 15.249237972318797, \"trend_4days\": 7.6246189861593985, \"trend_12days\": 2.5415396620531325, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7831, \"location\": \"Iran\", \"date\": \"2020-04-10T00:00:00\", \"total_cases\": 840.1879894933047, \"n_days\": 45, \"log_cases\": 6.733625663814382, \"trend_2days\": 15.595811562598769, \"trend_4days\": 7.797905781299384, \"trend_12days\": 2.599301927099795, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7832, \"location\": \"Iran\", \"date\": \"2020-04-11T00:00:00\", \"total_cases\": 862.8215144918265, \"n_days\": 46, \"log_cases\": 6.760207849869188, \"trend_2days\": 15.942385152878742, \"trend_4days\": 7.971192576439371, \"trend_12days\": 2.6570641921464566, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7833, \"location\": \"Iran\", \"date\": \"2020-04-12T00:00:00\", \"total_cases\": 883.2372743843418, \"n_days\": 47, \"log_cases\": 6.783593878418895, \"trend_2days\": 16.288958743158716, \"trend_4days\": 8.144479371579358, \"trend_12days\": 2.714826457193119, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7834, \"location\": \"Iran\", \"date\": \"2020-04-13T00:00:00\", \"total_cases\": 903.1601975866336, \"n_days\": 48, \"log_cases\": 6.805899943647672, \"trend_2days\": 16.635532333438686, \"trend_4days\": 8.317766166719343, \"trend_12days\": 2.772588722239781, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7835, \"location\": \"Iran\", \"date\": \"2020-04-14T00:00:00\", \"total_cases\": 922.5533213469348, \"n_days\": 49, \"log_cases\": 6.82714517516236, \"trend_2days\": 16.98210592371866, \"trend_4days\": 8.49105296185933, \"trend_12days\": 2.830350987286443, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7836, \"location\": \"Iran\", \"date\": \"2020-04-15T00:00:00\", \"total_cases\": 941.1825482373894, \"n_days\": 50, \"log_cases\": 6.8471371146470785, \"trend_2days\": 17.328679513998633, \"trend_4days\": 8.664339756999317, \"trend_12days\": 2.8881132523331052, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7837, \"location\": \"Iran\", \"date\": \"2020-04-16T00:00:00\", \"total_cases\": 960.9699413498695, \"n_days\": 51, \"log_cases\": 6.8679431299690075, \"trend_2days\": 17.675253104278603, \"trend_4days\": 8.837626552139302, \"trend_12days\": 2.9458755173797675, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7838, \"location\": \"Iran\", \"date\": \"2020-04-17T00:00:00\", \"total_cases\": 979.4389963160014, \"n_days\": 52, \"log_cases\": 6.886979955012216, \"trend_2days\": 18.021826694558577, \"trend_4days\": 9.010913347279288, \"trend_12days\": 3.0036377824264293, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7839, \"location\": \"Iran\", \"date\": \"2020-04-18T00:00:00\", \"total_cases\": 996.3679366251843, \"n_days\": 53, \"log_cases\": 6.904116603650255, \"trend_2days\": 18.36840028483855, \"trend_4days\": 9.184200142419275, \"trend_12days\": 3.0614000474730916, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7840, \"location\": \"Iran\", \"date\": \"2020-04-19T00:00:00\", \"total_cases\": 1012.9149284994438, \"n_days\": 54, \"log_cases\": 6.920587520958557, \"trend_2days\": 18.714973875118524, \"trend_4days\": 9.357486937559262, \"trend_12days\": 3.119162312519754, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7842, \"location\": \"Iran\", \"date\": \"2020-04-20T00:00:00\", \"total_cases\": 1028.8581954281794, \"n_days\": 55, \"log_cases\": 6.9362049182017556, \"trend_2days\": 19.061547465398494, \"trend_4days\": 9.530773732699247, \"trend_12days\": 3.1769245775664157, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7843, \"location\": \"Iran\", \"date\": \"2020-04-21T00:00:00\", \"total_cases\": 1044.8384251086818, \"n_days\": 56, \"log_cases\": 6.951617535323574, \"trend_2days\": 19.408121055678468, \"trend_4days\": 9.704060527839234, \"trend_12days\": 3.234686842613078, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7844, \"location\": \"Iran\", \"date\": \"2020-04-22T00:00:00\", \"total_cases\": 1059.5496003118583, \"n_days\": 57, \"log_cases\": 6.9655991914420765, \"trend_2days\": 19.75469464595844, \"trend_4days\": 9.87734732297922, \"trend_12days\": 3.29244910765974, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7845, \"location\": \"Iran\", \"date\": \"2020-04-23T00:00:00\", \"total_cases\": 1072.2401450851175, \"n_days\": 58, \"log_cases\": 6.977505332480534, \"trend_2days\": 20.101268236238415, \"trend_4days\": 10.050634118119207, \"trend_12days\": 3.350211372706402, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7846, \"location\": \"Iran\", \"date\": \"2020-04-24T00:00:00\", \"total_cases\": 1086.6309764396485, \"n_days\": 59, \"log_cases\": 6.99083734138608, \"trend_2days\": 20.447841826518385, \"trend_4days\": 10.223920913259192, \"trend_12days\": 3.4079736377530643, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7847, \"location\": \"Iran\", \"date\": \"2020-04-25T00:00:00\", \"total_cases\": 1100.6028966074896, \"n_days\": 60, \"log_cases\": 7.0036133964661795, \"trend_2days\": 20.79441541679836, \"trend_4days\": 10.39720770839918, \"trend_12days\": 3.465735902799726, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7848, \"location\": \"Iran\", \"date\": \"2020-04-26T00:00:00\", \"total_cases\": 1114.8089142031868, \"n_days\": 61, \"log_cases\": 7.01643829181119, \"trend_2days\": 21.140989007078332, \"trend_4days\": 10.570494503539166, \"trend_12days\": 3.5234981678463884, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 7849, \"location\": \"Iran\", \"date\": \"2020-04-27T00:00:00\", \"total_cases\": 1127.0189432034779, \"n_days\": 62, \"log_cases\": 7.027331322419791, \"trend_2days\": 21.487562597358306, \"trend_4days\": 10.743781298679153, \"trend_12days\": 3.5812604328930506, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}], \"data-2cf9e9c9dd4f97c8eda44cd9355d02e6\": [{\"label\": \"Doubles every 2 days\", \"x_coord\": 10, \"y_coord\": 6}, {\"label\": \"Doubles every 4 days\", \"x_coord\": 28, \"y_coord\": 6}, {\"label\": \"Doubles every 12 days\", \"x_coord\": 45, \"y_coord\": 3}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Stack data to get it to Altair dataframe format\n",
    "data_countries_pc2 = data_countries_pc.copy()\n",
    "for i in range(0,len(countries)):\n",
    "    data_countries_pc2[i] = data_countries_pc2[i].reset_index()\n",
    "    data_countries_pc2[i]['n_days'] = data_countries_pc2[i].index\n",
    "    data_countries_pc2[i]['log_cases'] = np.log(data_countries_pc2[i][\"total_cases\"])\n",
    "data_plot = data_countries_pc2[0]\n",
    "for i in range(1, len(countries)):    \n",
    "    data_plot = pd.concat([data_plot, data_countries_pc2[i]], axis=0)\n",
    "data_plot[\"trend_2days\"] = np.log(2)/2*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_4days\"] = np.log(2)/4*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_12days\"] = np.log(2)/12*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_2days_label\"] = \"Doubles every 2 days\"\n",
    "data_plot[\"trend_4days_label\"] = \"Doubles evey 4 days\"\n",
    "data_plot[\"trend_12days_label\"] = \"Doubles every 12 days\"\n",
    "\n",
    "\n",
    "# Plot it using Altair\n",
    "source = data_plot\n",
    "\n",
    "scales = alt.selection_interval(bind='scales', zoom=False)\n",
    "selection = alt.selection_multi(fields=['location'], bind='legend')\n",
    "\n",
    "base = alt.Chart(source, title = \"COVID-19 Confirmed Cases Per Million of Inhabitants\").encode(\n",
    "    x = alt.X('n_days:Q', title = \"Days passed since reaching 1 case per million\"),\n",
    "    y = alt.Y(\"log_cases:Q\",title = \"Log of confirmed cases per million\"),\n",
    "    color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                     scale=alt.Scale(scheme='tableau20')),\n",
    "    opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    ").properties(\n",
    "    width=chart_width,\n",
    "    height=chart_height\n",
    ")\n",
    "\n",
    "lines = base.mark_line().add_selection(\n",
    "    scales\n",
    ").add_selection(\n",
    "    selection\n",
    ")\n",
    "\n",
    "trend_2d = alt.Chart(source).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_2days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ").mark_line( strokeDash=[3,3], color=\"grey\")\n",
    "\n",
    "labels = pd.DataFrame([{'label': 'Doubles every 2 days', 'x_coord': 13, 'y_coord': 8.3},\n",
    "                       {'label': 'Doubles every 4 days', 'x_coord': 37.5, 'y_coord': 8.3},\n",
    "                       {'label': 'Doubles every 12 days', 'x_coord': 71, 'y_coord': 3},\n",
    "                      ])\n",
    "trend_label = (alt.Chart(labels)\n",
    "                    .mark_text(align='left', dx=-55, dy=-15, fontSize=12, color=\"grey\")\n",
    "                    .encode(x='x_coord:Q',\n",
    "                            y='y_coord:Q',\n",
    "                            text='label:N')\n",
    "                   )\n",
    "\n",
    "\n",
    "trend_4d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_4days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "trend_12d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_12days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "(\n",
    "(trend_2d  + trend_4d + trend_12d + trend_label + lines)\n",
    ".configure_title(fontSize=20)\n",
    ".configure_axis(labelFontSize=15,titleFontSize=18)\n",
    ")"
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   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "#hide_input\n",
    "\n",
    "#Added by Arun Gupta for Recovery and Death Rates\n",
    "# load data for infections, deaths, and recovered\n",
    "df_confirmed = load_individual_timeseries('confirmed')\n",
    "df_death = load_individual_timeseries('deaths')\n",
    "df_recovered = load_individual_timeseries('recovered')\n",
    "\n",
    "# clean the data\n",
    "df_confirmed = df_confirmed[~df_confirmed['country'].str.contains(' \\(total\\)')].drop(['state', 'type'], axis=1, ).reset_index()\n",
    "df_death = df_death[~df_death['country'].str.contains(' \\(total\\)')].drop(['state', 'type'], axis=1, ).reset_index()\n",
    "df_recovered = df_recovered[~df_recovered['country'].str.contains(' \\(total\\)')].drop(['state', 'type'], axis=1, ).reset_index()\n",
    "\n",
    "# get the data only for the latest date\n",
    "Latest_Date = df_confirmed['date'].max()\n",
    "df_confirmed = df_confirmed.loc[df_confirmed['date'] == Latest_Date]\n",
    "df_death = df_death.loc[df_death['date'] == Latest_Date]\n",
    "df_recovered = df_recovered.loc[df_recovered['date'] == Latest_Date]\n",
    "\n",
    "# remove column 'date' as it is no more required\n",
    "df_confirmed = df_confirmed.drop('date', axis=1)\n",
    "df_death = df_death.drop('date', axis=1)\n",
    "df_recovered = df_recovered.drop('date', axis=1)\n",
    "\n",
    "# aggregate data\n",
    "df_confirmed = (df_confirmed.sort_values(by=['country'])\n",
    "                            .groupby(['country'])\n",
    "                            .agg(sum)).reset_index()\n",
    "\n",
    "df_death = (df_death.sort_values(by=['country'])\n",
    "                    .groupby(['country'])\n",
    "                    .agg(sum)).reset_index()\n",
    "\n",
    "df_recovered = (df_recovered.sort_values(by=['country'])\n",
    "                            .groupby(['country'])\n",
    "                            .agg(sum)).reset_index()\n",
    "\n",
    "# rename the column for 'cases' in respective datasets\n",
    "df_confirmed = df_confirmed.rename(columns={\"cases\": \"infections\"})\n",
    "df_death = df_death.rename(columns={\"cases\": \"deaths\"})\n",
    "df_recovered = df_recovered.rename(columns={\"cases\": \"recovered\"})\n",
    "\n",
    "# (inner)join the datasets for 'confirmed' and 'death' on 'country'\n",
    "df_Master = pd.merge(df_confirmed, df_death, how='inner', on='country', left_on=None, right_on=None, left_index=False, right_index=False, sort=True)\n",
    "df_Master = pd.merge(df_Master, df_recovered, how='inner', on='country', left_on=None, right_on=None, left_index=False, right_index=False, sort=True)\n",
    "\n",
    "# rename the Countries\n",
    "df_Master['country'] = df_Master['country'].replace({'Bosnia and Herzegovina':'Bosnia Herzegovina',\n",
    "                                                     'Timor-Leste'           :'East Timor',\n",
    "                                                     \"Cote d'Ivoire\"         :'Ivory Coast',\n",
    "                                                     'Burma'                 :'Myanmar',\n",
    "                                                     'Korea, South'          :'South Korea',\n",
    "                                                     'Taiwan*'               :'Taiwan',\n",
    "                                                     'US'                    :'United States of America',\n",
    "                                                     'Holy See'              :'Vatican City'})\n",
    "\n",
    "## remove the countries that have less than 200 confirmed cases\n",
    "case_threshold = 200 \n",
    "keep_countries = df_Master.loc[(df_Master['infections'] > case_threshold)].country\n",
    "df_Master = df_Master.loc[df_Master['country'].isin(keep_countries)]\n",
    "\n",
    "# calculate 'deaths' and 'recovery' per 1000 infections\n",
    "df_Master['Deaths_per_1000'] = round(1000 * (df_Master['deaths']/df_Master['infections']), 2)\n",
    "df_Master['Recovered_per_1000'] = round(1000 * (df_Master['recovered']/df_Master['infections']), 2)\n",
    "\n",
    "# get countries with top 50 death rates\n",
    "df_top_death = df_Master.sort_values(by=['Deaths_per_1000'], ascending=False)\n",
    "df_top_death = df_top_death.head(50)\n",
    "\n",
    "# get countries with top 50 recovery rates\n",
    "df_top_recovered = df_Master.sort_values(by=['Recovered_per_1000'], ascending=False)\n",
    "df_top_recovered = df_top_recovered.head(50)\n",
    "\n",
    "# get rest of the countries\n",
    "df_ROW = df_Master.loc[~df_Master['country'].isin(df_top_death.country)]\n",
    "df_ROW = df_ROW.loc[~df_ROW['country'].isin(df_top_recovered.country)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the 50 countries that are facing highest rate of deaths per 1000 infections, across the world. As it is not so intuitive that the developed countries such as Belgium, France, Netherlands are among the top countries having highest death rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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\"Mexico\", \"infections\": 15529, \"deaths\": 1434, \"recovered\": 9086, \"Deaths_per_1000\": 92.34, \"Recovered_per_1000\": 585.1}, {\"country\": \"Honduras\", \"infections\": 661, \"deaths\": 61, \"recovered\": 79, \"Deaths_per_1000\": 92.28, \"Recovered_per_1000\": 119.52}, {\"country\": \"Indonesia\", \"infections\": 9096, \"deaths\": 765, \"recovered\": 1151, \"Deaths_per_1000\": 84.1, \"Recovered_per_1000\": 126.54}, {\"country\": \"Sudan\", \"infections\": 275, \"deaths\": 22, \"recovered\": 21, \"Deaths_per_1000\": 80.0, \"Recovered_per_1000\": 76.36}, {\"country\": \"San Marino\", \"infections\": 538, \"deaths\": 41, \"recovered\": 64, \"Deaths_per_1000\": 76.21, \"Recovered_per_1000\": 118.96}, {\"country\": \"Egypt\", \"infections\": 4782, \"deaths\": 337, \"recovered\": 1236, \"Deaths_per_1000\": 70.47, \"Recovered_per_1000\": 258.47}, {\"country\": \"Brazil\", \"infections\": 67446, \"deaths\": 4603, \"recovered\": 31142, \"Deaths_per_1000\": 68.25, \"Recovered_per_1000\": 461.73}, {\"country\": \"Burkina Faso\", \"infections\": 635, \"deaths\": 42, \"recovered\": 469, \"Deaths_per_1000\": 66.14, \"Recovered_per_1000\": 738.58}, {\"country\": \"Philippines\", \"infections\": 7777, \"deaths\": 511, \"recovered\": 932, \"Deaths_per_1000\": 65.71, \"Recovered_per_1000\": 119.84}, {\"country\": \"Iran\", \"infections\": 91472, \"deaths\": 5806, \"recovered\": 70933, \"Deaths_per_1000\": 63.47, \"Recovered_per_1000\": 775.46}, {\"country\": \"Congo (Kinshasa)\", \"infections\": 459, \"deaths\": 28, \"recovered\": 50, \"Deaths_per_1000\": 61.0, \"Recovered_per_1000\": 108.93}, {\"country\": \"Slovenia\", \"infections\": 1402, \"deaths\": 83, \"recovered\": 221, \"Deaths_per_1000\": 59.2, \"Recovered_per_1000\": 157.63}, {\"country\": \"Canada\", \"infections\": 49616, \"deaths\": 2841, \"recovered\": 18268, \"Deaths_per_1000\": 57.26, \"Recovered_per_1000\": 368.19}, {\"country\": \"Switzerland\", \"infections\": 29164, \"deaths\": 1665, \"recovered\": 22200, \"Deaths_per_1000\": 57.09, \"Recovered_per_1000\": 761.21}, {\"country\": \"United States of America\", \"infections\": 988197, \"deaths\": 56259, \"recovered\": 111424, \"Deaths_per_1000\": 56.93, \"Recovered_per_1000\": 112.75}, {\"country\": \"Romania\", \"infections\": 11339, \"deaths\": 641, \"recovered\": 3141, \"Deaths_per_1000\": 56.53, \"Recovered_per_1000\": 277.01}, {\"country\": \"Mali\", \"infections\": 408, \"deaths\": 23, \"recovered\": 113, \"Deaths_per_1000\": 56.37, \"Recovered_per_1000\": 276.96}, {\"country\": \"Ireland\", \"infections\": 19648, \"deaths\": 1102, \"recovered\": 9233, \"Deaths_per_1000\": 56.09, \"Recovered_per_1000\": 469.92}, {\"country\": \"China\", \"infections\": 82881, \"deaths\": 4633, \"recovered\": 77587, \"Deaths_per_1000\": 55.9, \"Recovered_per_1000\": 936.13}, {\"country\": \"Somalia\", \"infections\": 480, \"deaths\": 26, \"recovered\": 10, \"Deaths_per_1000\": 54.17, \"Recovered_per_1000\": 20.83}, {\"country\": \"Andorra\", \"infections\": 743, \"deaths\": 40, \"recovered\": 385, \"Deaths_per_1000\": 53.84, \"Recovered_per_1000\": 518.17}, {\"country\": \"Greece\", \"infections\": 2534, \"deaths\": 136, \"recovered\": 577, \"Deaths_per_1000\": 53.67, \"Recovered_per_1000\": 227.7}, {\"country\": \"Bolivia\", \"infections\": 1014, \"deaths\": 53, \"recovered\": 98, \"Deaths_per_1000\": 52.27, \"Recovered_per_1000\": 96.65}, {\"country\": \"Argentina\", \"infections\": 4003, \"deaths\": 197, \"recovered\": 1140, \"Deaths_per_1000\": 49.21, \"Recovered_per_1000\": 284.79}, {\"country\": \"Denmark\", \"infections\": 8896, \"deaths\": 427, \"recovered\": 6148, \"Deaths_per_1000\": 48.0, \"Recovered_per_1000\": 691.1}, {\"country\": \"Iraq\", \"infections\": 1847, \"deaths\": 88, \"recovered\": 1286, \"Deaths_per_1000\": 47.64, \"Recovered_per_1000\": 696.26}, {\"country\": \"Poland\", \"infections\": 11902, \"deaths\": 562, \"recovered\": 2466, \"Deaths_per_1000\": 47.22, \"Recovered_per_1000\": 207.19}, {\"country\": \"North Macedonia\", \"infections\": 1399, \"deaths\": 65, \"recovered\": 553, \"Deaths_per_1000\": 46.46, \"Recovered_per_1000\": 395.28}, {\"country\": \"Colombia\", \"infections\": 5597, \"deaths\": 253, \"recovered\": 1210, \"Deaths_per_1000\": 45.2, \"Recovered_per_1000\": 216.19}, {\"country\": \"Dominican Republic\", \"infections\": 6293, \"deaths\": 282, \"recovered\": 993, \"Deaths_per_1000\": 44.81, \"Recovered_per_1000\": 157.79}, {\"country\": \"Bulgaria\", \"infections\": 1363, \"deaths\": 58, \"recovered\": 206, \"Deaths_per_1000\": 42.55, \"Recovered_per_1000\": 151.14}, {\"country\": \"Niger\", \"infections\": 701, \"deaths\": 29, \"recovered\": 385, \"Deaths_per_1000\": 41.37, \"Recovered_per_1000\": 549.22}, {\"country\": \"Finland\", \"infections\": 4695, \"deaths\": 193, \"recovered\": 2500, \"Deaths_per_1000\": 41.11, \"Recovered_per_1000\": 532.48}, {\"country\": \"Tunisia\", \"infections\": 967, \"deaths\": 39, \"recovered\": 279, \"Deaths_per_1000\": 40.33, \"Recovered_per_1000\": 288.52}, {\"country\": \"Cuba\", \"infections\": 1389, \"deaths\": 56, \"recovered\": 525, \"Deaths_per_1000\": 40.32, \"Recovered_per_1000\": 377.97}, {\"country\": \"Paraguay\", \"infections\": 228, \"deaths\": 9, \"recovered\": 93, \"Deaths_per_1000\": 39.47, \"Recovered_per_1000\": 407.89}, {\"country\": \"Morocco\", \"infections\": 4120, \"deaths\": 162, \"recovered\": 695, \"Deaths_per_1000\": 39.32, \"Recovered_per_1000\": 168.69}, {\"country\": \"Portugal\", \"infections\": 24027, \"deaths\": 928, \"recovered\": 1357, \"Deaths_per_1000\": 38.62, \"Recovered_per_1000\": 56.48}, {\"country\": \"Germany\", \"infections\": 158758, \"deaths\": 6126, \"recovered\": 114500, \"Deaths_per_1000\": 38.59, \"Recovered_per_1000\": 721.22}, {\"country\": \"Kenya\", \"infections\": 363, \"deaths\": 14, \"recovered\": 114, \"Deaths_per_1000\": 38.57, \"Recovered_per_1000\": 314.05}, {\"country\": \"Bosnia Herzegovina\", 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      "text/plain": [
       "alt.Chart(...)"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "#hide_input\n",
    "# make the bar-chart for countries on \"deaths per 1000 infections\"\n",
    "alt.Chart(df_top_death).mark_bar(color='Orange').encode(\n",
    "    x= alt.Y('country:N', sort='-y', title=\"Countries\"),\n",
    "    y=alt.Y('Deaths_per_1000', title=\"Deaths per 1000 infected people\")\n",
    ").properties(\n",
    "    title='Countries with top death rates')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further, we move on to the countries with best recovery rate. The graph below shows 50 countries with highest rate of recovery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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\"recovered\": 645, \"Deaths_per_1000\": 18.26, \"Recovered_per_1000\": 905.9}, {\"country\": \"Mauritius\", \"infections\": 334, \"deaths\": 10, \"recovered\": 302, \"Deaths_per_1000\": 29.94, \"Recovered_per_1000\": 904.19}, {\"country\": \"Thailand\", \"infections\": 2931, \"deaths\": 52, \"recovered\": 2609, \"Deaths_per_1000\": 17.74, \"Recovered_per_1000\": 890.14}, {\"country\": \"Luxembourg\", \"infections\": 3729, \"deaths\": 88, \"recovered\": 3123, \"Deaths_per_1000\": 23.6, \"Recovered_per_1000\": 837.49}, {\"country\": \"Vietnam\", \"infections\": 270, \"deaths\": 0, \"recovered\": 225, \"Deaths_per_1000\": 0.0, \"Recovered_per_1000\": 833.33}, {\"country\": \"Australia\", \"infections\": 6721, \"deaths\": 83, \"recovered\": 5588, \"Deaths_per_1000\": 12.35, \"Recovered_per_1000\": 831.42}, {\"country\": \"New Zealand\", \"infections\": 1472, \"deaths\": 19, \"recovered\": 1214, \"Deaths_per_1000\": 12.91, \"Recovered_per_1000\": 824.73}, {\"country\": \"South Korea\", \"infections\": 10752, \"deaths\": 244, \"recovered\": 8854, \"Deaths_per_1000\": 22.69, \"Recovered_per_1000\": 823.47}, {\"country\": \"Austria\", \"infections\": 15274, \"deaths\": 549, \"recovered\": 12362, \"Deaths_per_1000\": 35.94, \"Recovered_per_1000\": 809.35}, {\"country\": \"Iran\", \"infections\": 91472, \"deaths\": 5806, \"recovered\": 70933, \"Deaths_per_1000\": 63.47, \"Recovered_per_1000\": 775.46}, {\"country\": \"Jordan\", \"infections\": 449, \"deaths\": 7, \"recovered\": 342, \"Deaths_per_1000\": 15.59, \"Recovered_per_1000\": 761.69}, {\"country\": \"Switzerland\", \"infections\": 29164, \"deaths\": 1665, \"recovered\": 22200, \"Deaths_per_1000\": 57.09, \"Recovered_per_1000\": 761.21}, {\"country\": \"Hong Kong\", \"infections\": 1037, \"deaths\": 4, \"recovered\": 787, \"Deaths_per_1000\": 3.86, \"Recovered_per_1000\": 758.92}, {\"country\": \"Burkina Faso\", \"infections\": 635, \"deaths\": 42, \"recovered\": 469, \"Deaths_per_1000\": 66.14, \"Recovered_per_1000\": 738.58}, {\"country\": \"Germany\", \"infections\": 158758, \"deaths\": 6126, \"recovered\": 114500, \"Deaths_per_1000\": 38.59, \"Recovered_per_1000\": 721.22}, {\"country\": \"Iraq\", \"infections\": 1847, \"deaths\": 88, \"recovered\": 1286, \"Deaths_per_1000\": 47.64, \"Recovered_per_1000\": 696.26}, {\"country\": \"Azerbaijan\", \"infections\": 1678, \"deaths\": 22, \"recovered\": 1162, \"Deaths_per_1000\": 13.11, \"Recovered_per_1000\": 692.49}, {\"country\": \"Denmark\", \"infections\": 8896, \"deaths\": 427, \"recovered\": 6148, \"Deaths_per_1000\": 48.0, \"Recovered_per_1000\": 691.1}, {\"country\": \"Malaysia\", \"infections\": 5820, \"deaths\": 99, \"recovered\": 3957, \"Deaths_per_1000\": 17.01, \"Recovered_per_1000\": 679.9}, {\"country\": \"Taiwan\", \"infections\": 429, \"deaths\": 6, \"recovered\": 290, \"Deaths_per_1000\": 13.99, \"Recovered_per_1000\": 675.99}, {\"country\": \"Malta\", \"infections\": 450, \"deaths\": 4, \"recovered\": 286, \"Deaths_per_1000\": 8.89, \"Recovered_per_1000\": 635.56}, {\"country\": \"Uruguay\", \"infections\": 620, \"deaths\": 15, \"recovered\": 386, \"Deaths_per_1000\": 24.19, \"Recovered_per_1000\": 622.58}, {\"country\": \"Montenegro\", \"infections\": 321, \"deaths\": 7, \"recovered\": 189, \"Deaths_per_1000\": 21.81, \"Recovered_per_1000\": 588.79}, {\"country\": \"Mexico\", \"infections\": 15529, \"deaths\": 1434, \"recovered\": 9086, \"Deaths_per_1000\": 92.34, \"Recovered_per_1000\": 585.1}, {\"country\": \"Albania\", \"infections\": 736, \"deaths\": 28, \"recovered\": 422, \"Deaths_per_1000\": 38.04, \"Recovered_per_1000\": 573.37}, {\"country\": \"Croatia\", \"infections\": 2039, \"deaths\": 59, \"recovered\": 1166, \"Deaths_per_1000\": 28.94, \"Recovered_per_1000\": 571.85}, {\"country\": \"Kyrgyzstan\", \"infections\": 695, \"deaths\": 8, \"recovered\": 395, \"Deaths_per_1000\": 11.51, \"Recovered_per_1000\": 568.35}, {\"country\": \"Niger\", \"infections\": 701, \"deaths\": 29, \"recovered\": 385, \"Deaths_per_1000\": 41.37, \"Recovered_per_1000\": 549.22}, {\"country\": \"Finland\", \"infections\": 4695, \"deaths\": 193, \"recovered\": 2500, \"Deaths_per_1000\": 41.11, \"Recovered_per_1000\": 532.48}, {\"country\": \"Chile\", \"infections\": 13813, \"deaths\": 198, \"recovered\": 7327, \"Deaths_per_1000\": 14.33, \"Recovered_per_1000\": 530.44}, {\"country\": \"Spain\", \"infections\": 229422, \"deaths\": 23521, \"recovered\": 120832, \"Deaths_per_1000\": 102.52, \"Recovered_per_1000\": 526.68}, {\"country\": \"Andorra\", \"infections\": 743, \"deaths\": 40, \"recovered\": 385, \"Deaths_per_1000\": 53.84, \"Recovered_per_1000\": 518.17}, {\"country\": \"Cameroon\", \"infections\": 1705, \"deaths\": 58, \"recovered\": 805, \"Deaths_per_1000\": 34.02, \"Recovered_per_1000\": 472.14}, {\"country\": \"Ireland\", \"infections\": 19648, \"deaths\": 1102, \"recovered\": 9233, \"Deaths_per_1000\": 56.09, \"Recovered_per_1000\": 469.92}, {\"country\": \"Armenia\", \"infections\": 1808, \"deaths\": 29, \"recovered\": 848, \"Deaths_per_1000\": 16.04, \"Recovered_per_1000\": 469.03}, {\"country\": \"Uzbekistan\", \"infections\": 1904, \"deaths\": 8, \"recovered\": 892, \"Deaths_per_1000\": 4.2, \"Recovered_per_1000\": 468.49}, {\"country\": \"Israel\", \"infections\": 15555, \"deaths\": 204, \"recovered\": 7200, \"Deaths_per_1000\": 13.11, \"Recovered_per_1000\": 462.87}, {\"country\": \"Brazil\", \"infections\": 67446, \"deaths\": 4603, \"recovered\": 31142, \"Deaths_per_1000\": 68.25, \"Recovered_per_1000\": 461.73}, {\"country\": \"Djibouti\", \"infections\": 1035, \"deaths\": 2, \"recovered\": 477, \"Deaths_per_1000\": 1.93, \"Recovered_per_1000\": 460.87}, {\"country\": \"Rwanda\", \"infections\": 207, \"deaths\": 0, \"recovered\": 93, \"Deaths_per_1000\": 0.0, \"Recovered_per_1000\": 449.28}, {\"country\": \"Bahrain\", \"infections\": 2723, \"deaths\": 8, \"recovered\": 1218, \"Deaths_per_1000\": 2.94, \"Recovered_per_1000\": 447.3}, {\"country\": \"Algeria\", \"infections\": 3517, \"deaths\": 432, \"recovered\": 1558, \"Deaths_per_1000\": 122.83, \"Recovered_per_1000\": 442.99}, {\"country\": \"Venezuela\", \"infections\": 329, \"deaths\": 10, \"recovered\": 142, \"Deaths_per_1000\": 30.4, \"Recovered_per_1000\": 431.61}, {\"country\": \"Ivory Coast\", \"infections\": 1164, \"deaths\": 14, \"recovered\": 499, \"Deaths_per_1000\": 12.03, \"Recovered_per_1000\": 428.69}, {\"country\": \"Bosnia Herzegovina\", \"infections\": 1565, \"deaths\": 60, \"recovered\": 659, \"Deaths_per_1000\": 38.34, \"Recovered_per_1000\": 421.09}, {\"country\": \"Costa Rica\", \"infections\": 697, \"deaths\": 6, \"recovered\": 287, \"Deaths_per_1000\": 8.61, \"Recovered_per_1000\": 411.76}, {\"country\": \"Paraguay\", \"infections\": 228, \"deaths\": 9, \"recovered\": 93, \"Deaths_per_1000\": 39.47, \"Recovered_per_1000\": 407.89}, {\"country\": \"North Macedonia\", \"infections\": 1399, \"deaths\": 65, \"recovered\": 553, \"Deaths_per_1000\": 46.46, \"Recovered_per_1000\": 395.28}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# make the bar-chart for countries on \"recovery per 1000 infections\"\n",
    "alt.Chart(df_top_recovered).mark_bar(color='#00CC66').encode(\n",
    "    x= alt.Y('country:N', sort='-y', title=\"Countries\"),\n",
    "    y=alt.Y('Recovered_per_1000', title=\"Recovered per 1000 infected people\")\n",
    ").properties(\n",
    "    title='Countries with top recovery rates')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we project the data from above graphs on to a scatter plot. The Orange and Green bubbles are from the above two bar charts. Additionally, the bubbles in Grey color are the rest of the countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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837.49}, {\"country\": \"Vietnam\", \"infections\": 270, \"deaths\": 0, \"recovered\": 225, \"Deaths_per_1000\": 0.0, \"Recovered_per_1000\": 833.33}, {\"country\": \"Australia\", \"infections\": 6721, \"deaths\": 83, \"recovered\": 5588, \"Deaths_per_1000\": 12.35, \"Recovered_per_1000\": 831.42}, {\"country\": \"New Zealand\", \"infections\": 1472, \"deaths\": 19, \"recovered\": 1214, \"Deaths_per_1000\": 12.91, \"Recovered_per_1000\": 824.73}, {\"country\": \"South Korea\", \"infections\": 10752, \"deaths\": 244, \"recovered\": 8854, \"Deaths_per_1000\": 22.69, \"Recovered_per_1000\": 823.47}, {\"country\": \"Austria\", \"infections\": 15274, \"deaths\": 549, \"recovered\": 12362, \"Deaths_per_1000\": 35.94, \"Recovered_per_1000\": 809.35}, {\"country\": \"Iran\", \"infections\": 91472, \"deaths\": 5806, \"recovered\": 70933, \"Deaths_per_1000\": 63.47, \"Recovered_per_1000\": 775.46}, {\"country\": \"Jordan\", \"infections\": 449, \"deaths\": 7, \"recovered\": 342, \"Deaths_per_1000\": 15.59, \"Recovered_per_1000\": 761.69}, {\"country\": \"Switzerland\", \"infections\": 29164, \"deaths\": 1665, \"recovered\": 22200, \"Deaths_per_1000\": 57.09, \"Recovered_per_1000\": 761.21}, {\"country\": \"Hong Kong\", \"infections\": 1037, \"deaths\": 4, \"recovered\": 787, \"Deaths_per_1000\": 3.86, \"Recovered_per_1000\": 758.92}, {\"country\": \"Burkina Faso\", \"infections\": 635, \"deaths\": 42, \"recovered\": 469, \"Deaths_per_1000\": 66.14, \"Recovered_per_1000\": 738.58}, {\"country\": \"Germany\", \"infections\": 158758, \"deaths\": 6126, \"recovered\": 114500, \"Deaths_per_1000\": 38.59, \"Recovered_per_1000\": 721.22}, {\"country\": \"Iraq\", \"infections\": 1847, \"deaths\": 88, \"recovered\": 1286, \"Deaths_per_1000\": 47.64, \"Recovered_per_1000\": 696.26}, {\"country\": \"Azerbaijan\", \"infections\": 1678, \"deaths\": 22, \"recovered\": 1162, \"Deaths_per_1000\": 13.11, \"Recovered_per_1000\": 692.49}, {\"country\": \"Denmark\", \"infections\": 8896, \"deaths\": 427, \"recovered\": 6148, \"Deaths_per_1000\": 48.0, \"Recovered_per_1000\": 691.1}, {\"country\": \"Malaysia\", \"infections\": 5820, \"deaths\": 99, \"recovered\": 3957, \"Deaths_per_1000\": 17.01, \"Recovered_per_1000\": 679.9}, {\"country\": \"Taiwan\", \"infections\": 429, \"deaths\": 6, \"recovered\": 290, \"Deaths_per_1000\": 13.99, \"Recovered_per_1000\": 675.99}, {\"country\": \"Malta\", \"infections\": 450, \"deaths\": 4, \"recovered\": 286, \"Deaths_per_1000\": 8.89, \"Recovered_per_1000\": 635.56}, {\"country\": \"Uruguay\", \"infections\": 620, \"deaths\": 15, \"recovered\": 386, \"Deaths_per_1000\": 24.19, \"Recovered_per_1000\": 622.58}, {\"country\": \"Montenegro\", \"infections\": 321, \"deaths\": 7, \"recovered\": 189, \"Deaths_per_1000\": 21.81, \"Recovered_per_1000\": 588.79}, {\"country\": \"Mexico\", \"infections\": 15529, \"deaths\": 1434, \"recovered\": 9086, \"Deaths_per_1000\": 92.34, \"Recovered_per_1000\": 585.1}, {\"country\": \"Albania\", \"infections\": 736, \"deaths\": 28, \"recovered\": 422, \"Deaths_per_1000\": 38.04, \"Recovered_per_1000\": 573.37}, {\"country\": \"Croatia\", \"infections\": 2039, \"deaths\": 59, \"recovered\": 1166, \"Deaths_per_1000\": 28.94, \"Recovered_per_1000\": 571.85}, {\"country\": \"Kyrgyzstan\", \"infections\": 695, \"deaths\": 8, \"recovered\": 395, \"Deaths_per_1000\": 11.51, \"Recovered_per_1000\": 568.35}, {\"country\": \"Niger\", \"infections\": 701, \"deaths\": 29, \"recovered\": 385, \"Deaths_per_1000\": 41.37, \"Recovered_per_1000\": 549.22}, {\"country\": \"Finland\", \"infections\": 4695, \"deaths\": 193, \"recovered\": 2500, \"Deaths_per_1000\": 41.11, \"Recovered_per_1000\": 532.48}, {\"country\": \"Chile\", \"infections\": 13813, \"deaths\": 198, \"recovered\": 7327, \"Deaths_per_1000\": 14.33, \"Recovered_per_1000\": 530.44}, {\"country\": \"Spain\", \"infections\": 229422, \"deaths\": 23521, \"recovered\": 120832, \"Deaths_per_1000\": 102.52, \"Recovered_per_1000\": 526.68}, {\"country\": \"Andorra\", \"infections\": 743, \"deaths\": 40, \"recovered\": 385, \"Deaths_per_1000\": 53.84, \"Recovered_per_1000\": 518.17}, {\"country\": \"Cameroon\", \"infections\": 1705, \"deaths\": 58, \"recovered\": 805, \"Deaths_per_1000\": 34.02, \"Recovered_per_1000\": 472.14}, {\"country\": \"Ireland\", \"infections\": 19648, \"deaths\": 1102, \"recovered\": 9233, \"Deaths_per_1000\": 56.09, \"Recovered_per_1000\": 469.92}, {\"country\": \"Armenia\", \"infections\": 1808, \"deaths\": 29, \"recovered\": 848, \"Deaths_per_1000\": 16.04, \"Recovered_per_1000\": 469.03}, {\"country\": \"Uzbekistan\", \"infections\": 1904, \"deaths\": 8, \"recovered\": 892, \"Deaths_per_1000\": 4.2, \"Recovered_per_1000\": 468.49}, {\"country\": \"Israel\", \"infections\": 15555, \"deaths\": 204, \"recovered\": 7200, \"Deaths_per_1000\": 13.11, \"Recovered_per_1000\": 462.87}, {\"country\": \"Brazil\", \"infections\": 67446, \"deaths\": 4603, \"recovered\": 31142, \"Deaths_per_1000\": 68.25, \"Recovered_per_1000\": 461.73}, {\"country\": \"Djibouti\", \"infections\": 1035, \"deaths\": 2, \"recovered\": 477, \"Deaths_per_1000\": 1.93, \"Recovered_per_1000\": 460.87}, {\"country\": \"Rwanda\", \"infections\": 207, \"deaths\": 0, \"recovered\": 93, \"Deaths_per_1000\": 0.0, \"Recovered_per_1000\": 449.28}, {\"country\": \"Bahrain\", \"infections\": 2723, \"deaths\": 8, \"recovered\": 1218, \"Deaths_per_1000\": 2.94, \"Recovered_per_1000\": 447.3}, {\"country\": \"Algeria\", \"infections\": 3517, \"deaths\": 432, \"recovered\": 1558, \"Deaths_per_1000\": 122.83, \"Recovered_per_1000\": 442.99}, {\"country\": \"Venezuela\", \"infections\": 329, \"deaths\": 10, \"recovered\": 142, \"Deaths_per_1000\": 30.4, \"Recovered_per_1000\": 431.61}, {\"country\": \"Ivory Coast\", \"infections\": 1164, \"deaths\": 14, \"recovered\": 499, \"Deaths_per_1000\": 12.03, \"Recovered_per_1000\": 428.69}, {\"country\": \"Bosnia Herzegovina\", \"infections\": 1565, \"deaths\": 60, \"recovered\": 659, \"Deaths_per_1000\": 38.34, \"Recovered_per_1000\": 421.09}, {\"country\": \"Costa Rica\", \"infections\": 697, \"deaths\": 6, \"recovered\": 287, \"Deaths_per_1000\": 8.61, \"Recovered_per_1000\": 411.76}, {\"country\": \"Paraguay\", \"infections\": 228, \"deaths\": 9, \"recovered\": 93, \"Deaths_per_1000\": 39.47, \"Recovered_per_1000\": 407.89}, {\"country\": \"North Macedonia\", \"infections\": 1399, \"deaths\": 65, \"recovered\": 553, \"Deaths_per_1000\": 46.46, \"Recovered_per_1000\": 395.28}], \"data-edb1b36d107955237678b9b2175845b1\": [{\"country\": \"Belgium\", \"infections\": 46687, \"deaths\": 7207, \"recovered\": 10878, \"Deaths_per_1000\": 154.37, \"Recovered_per_1000\": 233.0}, {\"country\": \"France\", \"infections\": 165963, \"deaths\": 23327, \"recovered\": 46293, \"Deaths_per_1000\": 140.56, \"Recovered_per_1000\": 278.94}, {\"country\": \"Italy\", \"infections\": 199414, \"deaths\": 26977, \"recovered\": 66624, \"Deaths_per_1000\": 135.28, \"Recovered_per_1000\": 334.1}, {\"country\": \"United Kingdom\", \"infections\": 158348, \"deaths\": 21157, \"recovered\": 807, \"Deaths_per_1000\": 133.61, \"Recovered_per_1000\": 5.1}, {\"country\": \"Algeria\", \"infections\": 3517, \"deaths\": 432, \"recovered\": 1558, \"Deaths_per_1000\": 122.83, \"Recovered_per_1000\": 442.99}, {\"country\": \"Sweden\", \"infections\": 18926, \"deaths\": 2274, \"recovered\": 1005, \"Deaths_per_1000\": 120.15, \"Recovered_per_1000\": 53.1}, {\"country\": \"Netherlands\", \"infections\": 38440, \"deaths\": 4534, \"recovered\": 117, \"Deaths_per_1000\": 117.95, \"Recovered_per_1000\": 3.04}, {\"country\": \"Hungary\", \"infections\": 2583, \"deaths\": 280, \"recovered\": 498, \"Deaths_per_1000\": 108.4, \"Recovered_per_1000\": 192.8}, {\"country\": \"Spain\", \"infections\": 229422, \"deaths\": 23521, \"recovered\": 120832, \"Deaths_per_1000\": 102.52, \"Recovered_per_1000\": 526.68}, {\"country\": \"Mexico\", \"infections\": 15529, \"deaths\": 1434, \"recovered\": 9086, \"Deaths_per_1000\": 92.34, \"Recovered_per_1000\": 585.1}, {\"country\": \"Honduras\", \"infections\": 661, \"deaths\": 61, \"recovered\": 79, \"Deaths_per_1000\": 92.28, \"Recovered_per_1000\": 119.52}, {\"country\": \"Indonesia\", \"infections\": 9096, \"deaths\": 765, \"recovered\": 1151, \"Deaths_per_1000\": 84.1, \"Recovered_per_1000\": 126.54}, {\"country\": \"Sudan\", \"infections\": 275, \"deaths\": 22, \"recovered\": 21, \"Deaths_per_1000\": 80.0, \"Recovered_per_1000\": 76.36}, {\"country\": \"San Marino\", \"infections\": 538, \"deaths\": 41, \"recovered\": 64, \"Deaths_per_1000\": 76.21, \"Recovered_per_1000\": 118.96}, {\"country\": \"Egypt\", \"infections\": 4782, \"deaths\": 337, \"recovered\": 1236, \"Deaths_per_1000\": 70.47, \"Recovered_per_1000\": 258.47}, {\"country\": \"Brazil\", \"infections\": 67446, \"deaths\": 4603, \"recovered\": 31142, \"Deaths_per_1000\": 68.25, \"Recovered_per_1000\": 461.73}, {\"country\": \"Burkina Faso\", \"infections\": 635, \"deaths\": 42, \"recovered\": 469, \"Deaths_per_1000\": 66.14, \"Recovered_per_1000\": 738.58}, {\"country\": \"Philippines\", \"infections\": 7777, \"deaths\": 511, \"recovered\": 932, \"Deaths_per_1000\": 65.71, \"Recovered_per_1000\": 119.84}, {\"country\": \"Iran\", \"infections\": 91472, \"deaths\": 5806, \"recovered\": 70933, \"Deaths_per_1000\": 63.47, \"Recovered_per_1000\": 775.46}, {\"country\": \"Congo (Kinshasa)\", \"infections\": 459, \"deaths\": 28, \"recovered\": 50, \"Deaths_per_1000\": 61.0, \"Recovered_per_1000\": 108.93}, {\"country\": \"Slovenia\", \"infections\": 1402, \"deaths\": 83, \"recovered\": 221, \"Deaths_per_1000\": 59.2, \"Recovered_per_1000\": 157.63}, {\"country\": \"Canada\", \"infections\": 49616, \"deaths\": 2841, \"recovered\": 18268, \"Deaths_per_1000\": 57.26, \"Recovered_per_1000\": 368.19}, {\"country\": \"Switzerland\", \"infections\": 29164, \"deaths\": 1665, \"recovered\": 22200, \"Deaths_per_1000\": 57.09, \"Recovered_per_1000\": 761.21}, {\"country\": \"United States of America\", \"infections\": 988197, \"deaths\": 56259, \"recovered\": 111424, \"Deaths_per_1000\": 56.93, \"Recovered_per_1000\": 112.75}, {\"country\": \"Romania\", \"infections\": 11339, \"deaths\": 641, \"recovered\": 3141, \"Deaths_per_1000\": 56.53, \"Recovered_per_1000\": 277.01}, {\"country\": \"Mali\", \"infections\": 408, \"deaths\": 23, \"recovered\": 113, \"Deaths_per_1000\": 56.37, \"Recovered_per_1000\": 276.96}, {\"country\": \"Ireland\", \"infections\": 19648, \"deaths\": 1102, \"recovered\": 9233, \"Deaths_per_1000\": 56.09, \"Recovered_per_1000\": 469.92}, {\"country\": \"China\", \"infections\": 82881, \"deaths\": 4633, \"recovered\": 77587, \"Deaths_per_1000\": 55.9, \"Recovered_per_1000\": 936.13}, {\"country\": \"Somalia\", \"infections\": 480, \"deaths\": 26, \"recovered\": 10, \"Deaths_per_1000\": 54.17, \"Recovered_per_1000\": 20.83}, {\"country\": \"Andorra\", \"infections\": 743, \"deaths\": 40, \"recovered\": 385, \"Deaths_per_1000\": 53.84, \"Recovered_per_1000\": 518.17}, {\"country\": \"Greece\", \"infections\": 2534, \"deaths\": 136, \"recovered\": 577, \"Deaths_per_1000\": 53.67, \"Recovered_per_1000\": 227.7}, {\"country\": \"Bolivia\", \"infections\": 1014, \"deaths\": 53, \"recovered\": 98, \"Deaths_per_1000\": 52.27, \"Recovered_per_1000\": 96.65}, {\"country\": \"Argentina\", \"infections\": 4003, \"deaths\": 197, \"recovered\": 1140, \"Deaths_per_1000\": 49.21, \"Recovered_per_1000\": 284.79}, {\"country\": \"Denmark\", \"infections\": 8896, \"deaths\": 427, \"recovered\": 6148, \"Deaths_per_1000\": 48.0, \"Recovered_per_1000\": 691.1}, {\"country\": \"Iraq\", \"infections\": 1847, \"deaths\": 88, \"recovered\": 1286, \"Deaths_per_1000\": 47.64, \"Recovered_per_1000\": 696.26}, {\"country\": \"Poland\", \"infections\": 11902, \"deaths\": 562, \"recovered\": 2466, \"Deaths_per_1000\": 47.22, \"Recovered_per_1000\": 207.19}, {\"country\": \"North Macedonia\", \"infections\": 1399, \"deaths\": 65, \"recovered\": 553, \"Deaths_per_1000\": 46.46, \"Recovered_per_1000\": 395.28}, {\"country\": \"Colombia\", \"infections\": 5597, \"deaths\": 253, \"recovered\": 1210, \"Deaths_per_1000\": 45.2, \"Recovered_per_1000\": 216.19}, {\"country\": \"Dominican Republic\", \"infections\": 6293, \"deaths\": 282, \"recovered\": 993, \"Deaths_per_1000\": 44.81, \"Recovered_per_1000\": 157.79}, {\"country\": \"Bulgaria\", \"infections\": 1363, \"deaths\": 58, \"recovered\": 206, \"Deaths_per_1000\": 42.55, \"Recovered_per_1000\": 151.14}, {\"country\": \"Niger\", \"infections\": 701, \"deaths\": 29, \"recovered\": 385, \"Deaths_per_1000\": 41.37, \"Recovered_per_1000\": 549.22}, {\"country\": \"Finland\", \"infections\": 4695, \"deaths\": 193, \"recovered\": 2500, \"Deaths_per_1000\": 41.11, \"Recovered_per_1000\": 532.48}, {\"country\": \"Tunisia\", \"infections\": 967, \"deaths\": 39, \"recovered\": 279, \"Deaths_per_1000\": 40.33, \"Recovered_per_1000\": 288.52}, {\"country\": \"Cuba\", \"infections\": 1389, \"deaths\": 56, \"recovered\": 525, \"Deaths_per_1000\": 40.32, \"Recovered_per_1000\": 377.97}, {\"country\": \"Paraguay\", \"infections\": 228, \"deaths\": 9, \"recovered\": 93, \"Deaths_per_1000\": 39.47, \"Recovered_per_1000\": 407.89}, {\"country\": \"Morocco\", \"infections\": 4120, \"deaths\": 162, \"recovered\": 695, \"Deaths_per_1000\": 39.32, \"Recovered_per_1000\": 168.69}, {\"country\": \"Portugal\", \"infections\": 24027, \"deaths\": 928, \"recovered\": 1357, \"Deaths_per_1000\": 38.62, \"Recovered_per_1000\": 56.48}, {\"country\": \"Germany\", \"infections\": 158758, \"deaths\": 6126, \"recovered\": 114500, \"Deaths_per_1000\": 38.59, \"Recovered_per_1000\": 721.22}, {\"country\": \"Kenya\", \"infections\": 363, \"deaths\": 14, \"recovered\": 114, \"Deaths_per_1000\": 38.57, \"Recovered_per_1000\": 314.05}, {\"country\": \"Bosnia Herzegovina\", \"infections\": 1565, \"deaths\": 60, \"recovered\": 659, \"Deaths_per_1000\": 38.34, \"Recovered_per_1000\": 421.09}], \"data-1e3558a3811c18ee70528cc5f6d3fda7\": [{\"country\": \"Afghanistan\", \"infections\": 1703, \"deaths\": 57, \"recovered\": 220, \"Deaths_per_1000\": 33.47, \"Recovered_per_1000\": 129.18}, {\"country\": \"Bangladesh\", \"infections\": 5913, \"deaths\": 152, \"recovered\": 131, \"Deaths_per_1000\": 25.71, \"Recovered_per_1000\": 22.15}, {\"country\": \"Belarus\", \"infections\": 11289, \"deaths\": 75, \"recovered\": 1740, \"Deaths_per_1000\": 6.64, \"Recovered_per_1000\": 154.13}, {\"country\": \"Cyprus\", \"infections\": 822, \"deaths\": 15, \"recovered\": 148, \"Deaths_per_1000\": 18.25, \"Recovered_per_1000\": 180.05}, {\"country\": \"Czechia\", \"infections\": 7445, \"deaths\": 223, \"recovered\": 2826, \"Deaths_per_1000\": 29.95, \"Recovered_per_1000\": 379.58}, {\"country\": \"Ecuador\", \"infections\": 23240, \"deaths\": 663, \"recovered\": 1557, \"Deaths_per_1000\": 28.53, \"Recovered_per_1000\": 67.0}, {\"country\": \"El Salvador\", \"infections\": 323, \"deaths\": 8, \"recovered\": 89, \"Deaths_per_1000\": 24.77, \"Recovered_per_1000\": 275.54}, {\"country\": \"Equatorial Guinea\", \"infections\": 258, \"deaths\": 1, \"recovered\": 9, \"Deaths_per_1000\": 3.88, \"Recovered_per_1000\": 34.88}, {\"country\": \"Estonia\", \"infections\": 1647, \"deaths\": 50, \"recovered\": 233, \"Deaths_per_1000\": 30.36, \"Recovered_per_1000\": 141.47}, {\"country\": \"Gabon\", \"infections\": 211, \"deaths\": 3, \"recovered\": 43, \"Deaths_per_1000\": 14.22, \"Recovered_per_1000\": 203.79}, {\"country\": \"Georgia\", \"infections\": 497, \"deaths\": 6, \"recovered\": 156, \"Deaths_per_1000\": 12.07, \"Recovered_per_1000\": 313.88}, {\"country\": \"Ghana\", \"infections\": 1550, \"deaths\": 11, \"recovered\": 155, \"Deaths_per_1000\": 7.1, \"Recovered_per_1000\": 100.0}, {\"country\": \"Guatemala\", \"infections\": 530, \"deaths\": 15, \"recovered\": 49, \"Deaths_per_1000\": 28.3, \"Recovered_per_1000\": 92.45}, {\"country\": \"Guinea\", \"infections\": 1163, \"deaths\": 7, \"recovered\": 246, \"Deaths_per_1000\": 6.02, \"Recovered_per_1000\": 211.52}, {\"country\": \"India\", \"infections\": 29451, \"deaths\": 939, \"recovered\": 7137, \"Deaths_per_1000\": 31.88, \"Recovered_per_1000\": 242.33}, {\"country\": \"Jamaica\", \"infections\": 364, \"deaths\": 7, \"recovered\": 29, \"Deaths_per_1000\": 19.23, \"Recovered_per_1000\": 79.67}, {\"country\": \"Japan\", \"infections\": 14153, \"deaths\": 385, \"recovered\": 1899, \"Deaths_per_1000\": 27.2, \"Recovered_per_1000\": 134.18}, {\"country\": \"Kazakhstan\", \"infections\": 2835, \"deaths\": 25, \"recovered\": 720, \"Deaths_per_1000\": 8.82, \"Recovered_per_1000\": 253.97}, {\"country\": \"Kosovo\", \"infections\": 510, \"deaths\": 12, \"recovered\": 93, \"Deaths_per_1000\": 23.53, \"Recovered_per_1000\": 182.35}, {\"country\": \"Kuwait\", \"infections\": 3288, \"deaths\": 22, \"recovered\": 1012, \"Deaths_per_1000\": 6.69, \"Recovered_per_1000\": 307.79}, {\"country\": \"Latvia\", \"infections\": 818, \"deaths\": 13, \"recovered\": 267, \"Deaths_per_1000\": 15.89, \"Recovered_per_1000\": 326.41}, {\"country\": \"Lebanon\", \"infections\": 710, \"deaths\": 24, \"recovered\": 145, \"Deaths_per_1000\": 33.8, \"Recovered_per_1000\": 204.23}, {\"country\": \"Lithuania\", \"infections\": 1449, \"deaths\": 41, \"recovered\": 474, \"Deaths_per_1000\": 28.3, \"Recovered_per_1000\": 327.12}, {\"country\": \"Maldives\", \"infections\": 226, \"deaths\": 0, \"recovered\": 17, \"Deaths_per_1000\": 0.0, \"Recovered_per_1000\": 75.22}, {\"country\": \"Moldova\", \"infections\": 3481, \"deaths\": 102, \"recovered\": 925, \"Deaths_per_1000\": 29.3, \"Recovered_per_1000\": 265.73}, {\"country\": \"Nigeria\", \"infections\": 1337, \"deaths\": 40, \"recovered\": 255, \"Deaths_per_1000\": 29.92, \"Recovered_per_1000\": 190.73}, {\"country\": \"Norway\", \"infections\": 7599, \"deaths\": 205, \"recovered\": 32, \"Deaths_per_1000\": 26.98, \"Recovered_per_1000\": 4.21}, {\"country\": \"Oman\", \"infections\": 2049, \"deaths\": 10, \"recovered\": 364, \"Deaths_per_1000\": 4.88, \"Recovered_per_1000\": 177.65}, {\"country\": \"Pakistan\", \"infections\": 13915, \"deaths\": 292, \"recovered\": 3029, \"Deaths_per_1000\": 20.98, \"Recovered_per_1000\": 217.68}, {\"country\": \"Panama\", \"infections\": 6021, \"deaths\": 167, \"recovered\": 455, \"Deaths_per_1000\": 27.74, \"Recovered_per_1000\": 75.57}, {\"country\": \"Peru\", \"infections\": 28699, \"deaths\": 782, \"recovered\": 8425, \"Deaths_per_1000\": 27.25, \"Recovered_per_1000\": 293.56}, {\"country\": \"Qatar\", \"infections\": 11244, \"deaths\": 10, \"recovered\": 1066, \"Deaths_per_1000\": 0.89, \"Recovered_per_1000\": 94.81}, {\"country\": \"Russia\", \"infections\": 87147, \"deaths\": 794, \"recovered\": 7346, \"Deaths_per_1000\": 9.11, \"Recovered_per_1000\": 84.29}, {\"country\": \"Saudi Arabia\", \"infections\": 18811, \"deaths\": 144, \"recovered\": 2531, \"Deaths_per_1000\": 7.66, \"Recovered_per_1000\": 134.55}, {\"country\": \"Senegal\", \"infections\": 736, \"deaths\": 9, \"recovered\": 284, \"Deaths_per_1000\": 12.23, \"Recovered_per_1000\": 385.87}, {\"country\": \"Serbia\", \"infections\": 6630, \"deaths\": 125, \"recovered\": 870, \"Deaths_per_1000\": 18.85, \"Recovered_per_1000\": 131.22}, {\"country\": \"Singapore\", \"infections\": 14423, \"deaths\": 14, \"recovered\": 1095, \"Deaths_per_1000\": 0.97, \"Recovered_per_1000\": 75.92}, {\"country\": \"Slovakia\", \"infections\": 1381, \"deaths\": 18, \"recovered\": 403, \"Deaths_per_1000\": 13.03, \"Recovered_per_1000\": 291.82}, {\"country\": \"South Africa\", \"infections\": 4793, \"deaths\": 90, \"recovered\": 1473, \"Deaths_per_1000\": 18.78, \"Recovered_per_1000\": 307.32}, {\"country\": \"Sri Lanka\", \"infections\": 588, \"deaths\": 7, \"recovered\": 126, \"Deaths_per_1000\": 11.9, \"Recovered_per_1000\": 214.29}, {\"country\": \"Tanzania\", \"infections\": 299, \"deaths\": 10, \"recovered\": 48, \"Deaths_per_1000\": 33.44, \"Recovered_per_1000\": 160.54}, {\"country\": \"Turkey\", \"infections\": 112261, \"deaths\": 2900, \"recovered\": 33791, \"Deaths_per_1000\": 25.83, \"Recovered_per_1000\": 301.0}, {\"country\": \"Ukraine\", \"infections\": 9009, \"deaths\": 220, \"recovered\": 864, \"Deaths_per_1000\": 24.42, \"Recovered_per_1000\": 95.9}, {\"country\": \"United Arab Emirates\", \"infections\": 10839, \"deaths\": 82, \"recovered\": 2090, \"Deaths_per_1000\": 7.57, \"Recovered_per_1000\": 192.82}, {\"country\": \"West Bank and Gaza\", \"infections\": 342, \"deaths\": 2, \"recovered\": 83, \"Deaths_per_1000\": 5.85, \"Recovered_per_1000\": 242.69}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# make the scatter plot for \"Death Rates by Population Density\"\n",
    "chart_high_recovery = alt.Chart(df_top_recovered).mark_circle(size=150, color='#00CC66').encode(\n",
    "    x=alt.Y('Recovered_per_1000',  title=\"Recovery per 1000 infections\"),\n",
    "    y=alt.Y('Deaths_per_1000',  title=\"Deaths per 1000 infections\"),\n",
    "    tooltip=['country', 'Recovered_per_1000', 'Deaths_per_1000']\n",
    ").properties(\n",
    "    width=700,\n",
    "    height=450)\n",
    "\n",
    "chart_high_death = alt.Chart(df_top_death).mark_circle(size=150, color='Orange').encode(\n",
    "    x=alt.Y('Recovered_per_1000',  title=\"Recovery per 1000 infections\"),\n",
    "    y=alt.Y('Deaths_per_1000',  title=\"Deaths per 1000 infections\"),\n",
    "    tooltip=['country', 'Recovered_per_1000', 'Deaths_per_1000']\n",
    ").properties(\n",
    "    width=700,\n",
    "    height=450)\n",
    "\n",
    "chart_rest = alt.Chart(df_ROW).mark_circle(size=150, color='#C3C3C3').encode(\n",
    "    x=alt.Y('Recovered_per_1000'),\n",
    "    y=alt.Y('Deaths_per_1000'),\n",
    "    tooltip=['country', 'Recovered_per_1000', 'Deaths_per_1000']\n",
    ").properties(\n",
    "    width=700,\n",
    "    height=450)\n",
    "\n",
    "(chart_high_recovery + chart_high_death + chart_rest).interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021479,
     "end_time": "2020-04-28T12:13:18.489956",
     "exception": false,
     "start_time": "2020-04-28T12:13:18.468477",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021776,
     "end_time": "2020-04-28T12:13:18.534057",
     "exception": false,
     "start_time": "2020-04-28T12:13:18.512281",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Last Available Cases Per Million By Country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-28T12:13:18.586833Z",
     "iopub.status.busy": "2020-04-28T12:13:18.585360Z",
     "iopub.status.idle": "2020-04-28T12:13:18.613218Z",
     "shell.execute_reply": "2020-04-28T12:13:18.612574Z"
    },
    "papermill": {
     "duration": 0.057634,
     "end_time": "2020-04-28T12:13:18.613360",
     "exception": false,
     "start_time": "2020-04-28T12:13:18.555726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Cases per Million</th>\n",
       "      <th>Log of Cases per Million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>322.263619</td>\n",
       "      <td>5.775370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>59.536687</td>\n",
       "      <td>4.086593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>2468.719524</td>\n",
       "      <td>7.811455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>1933.379942</td>\n",
       "      <td>7.567025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>1127.018943</td>\n",
       "      <td>7.027331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>3359.405848</td>\n",
       "      <td>8.119519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>111.017460</td>\n",
       "      <td>4.709687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>2326.055107</td>\n",
       "      <td>7.751929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>2526.430838</td>\n",
       "      <td>7.834563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>210.897087</td>\n",
       "      <td>5.351370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>4949.312111</td>\n",
       "      <td>8.507004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>2392.629229</td>\n",
       "      <td>7.780148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>3045.671596</td>\n",
       "      <td>8.021477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  Cases per Million  Log of Cases per Million\n",
       "Country                                                               \n",
       "Brazil         2020-04-27         322.263619                  5.775370\n",
       "China          2020-04-27          59.536687                  4.086593\n",
       "France         2020-04-27        2468.719524                  7.811455\n",
       "Germany        2020-04-27        1933.379942                  7.567025\n",
       "Iran           2020-04-27        1127.018943                  7.027331\n",
       "Italy          2020-04-27        3359.405848                  8.119519\n",
       "Japan          2020-04-27         111.017460                  4.709687\n",
       "Portugal       2020-04-27        2326.055107                  7.751929\n",
       "Singapore      2020-04-27        2526.430838                  7.834563\n",
       "South Korea    2020-04-27         210.897087                  5.351370\n",
       "Spain          2020-04-27        4949.312111                  8.507004\n",
       "United Kingdom 2020-04-27        2392.629229                  7.780148\n",
       "United States  2020-04-27        3045.671596                  8.021477"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "label = 'Cases'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name = f'{label} per Million'\n",
    "temp.columns = ['Country', 'date', metric_name]\n",
    "# temp.loc[:, 'month'] = temp.date.dt.strftime('%Y-%m')\n",
    "temp.loc[:, f'Log of {label} per Million'] = temp[f'{label} per Million'].apply(lambda x: np.log(x))\n",
    "\n",
    "temp.groupby('Country').last()"
   ]
  },
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "This analysis was conducted by [Joao B. Duarte](http://jbduarte.com). Assitance with creating visualizations were provided by [Hamel Husain](https://twitter.com/HamelHusain). \n",
    "The highest Recovery and Death rates were added by [Arun Gupta](https://www.linkedin.com/in/arungupta21/). \n",
    "Relevant sources are listed below: \n",
    "\n",
    "\n",
    "1. [\"2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE\"](https://systems.jhu.edu/research/public-health/ncov/) [GitHub repository](https://github.com/CSSEGISandData/COVID-19). \n",
    "\n",
    "2. [Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), \"The Next Generation of the Penn World Table\" American Economic Review, 105(10), 3150-3182](https://www.rug.nl/ggdc/productivity/pwt/related-research)"
   ]
  }
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